Quantifying the Quality of Legal Services: Data Science Lessons from Avvo

2018 ◽  
pp. 153-171 ◽  
Author(s):  
Nika Kabiri ◽  
Ed Sarausad ◽  
Rahul Dodhia
Keyword(s):  
Data Science ◽  
Legal Services

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals An AI-Based System for Formative and Summative Assessment in Data Science Courses

2020 ◽  
Author(s):  
Pierpaolo Vittorini ◽  
Stefano Menini ◽  
Sara Tonelli
Keyword(s):  
Data Science ◽  
Ad Hoc ◽  
Online Courses ◽  
Assessment Tools ◽  
Science Courses ◽  
Summative Assessments ◽  
Assessment Activity ◽  
Automated Grading ◽  
Automated Feedback

AbstractMassive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper reports on a system and related experiments finalised to improve both the performance and quality of formative and summative assessments in specific data science courses. The system is developed to automatically grade assignments composed of R commands commented with short sentences written in natural language. In our opinion, the use of the system can (i) shorten the correction times and reduce the possibility of errors and (ii) support the students while solving the exercises assigned during the course through automated feedback. To investigate these aims, an ad-hoc experiment was conducted in three courses containing the specific topic of statistical analysis of health data. Our evaluation demonstrated that automated grading has an acceptable correlation with human grading. Furthermore, the students who used the tool did not report usability issues, and those that used it for more than half of the exercises obtained (on average) higher grades in the exam. Finally, the use of the system reduced the correction time and assisted the professor in identifying correction errors.

scholarly journals Altruism, Solidarity and Responsibility from a Committed Sociology: Contributions to Society

2021 ◽  
Author(s):  
Estrella Gualda
Keyword(s):  
International Development ◽  
Data Science ◽  
Collective Responsibility ◽  
Focus Of Attention ◽  
Sociological Research ◽  
Refugee Crisis ◽  
Research And Practice ◽  
Humanitarian Emergencies ◽  
History Of

AbstractA careful look at the international development of Sociology highlights the centrality that the study of social problems and the approach to possible solutions to them have had in the history of this discipline, not infrequently for the sake of better social integration, stability, development, social change or even modernity. Recent approaches suggest shifting this focus of attention, arguing about the deficit in sociological research and practice concerning theor etical frameworks that pay attention to the positive aspects. This text reflects on the contributions that altruism, solidarity, and collective responsibility can have to improve the quality of life in contemporary societies and face humanitarian emergencies with a certain degree of success. For instance, the so-called refugee crisis or the current COVID-19 pandemic poses significant challenges for societies. This article also explores briefly new roles of data science in connection with responsibility and altruism. The text invites us to revisit sociology, thinking about the lights more than the shadows.

scholarly journals A Data Science Analysis of Academic Staff Workload Profiles in Spanish Universities: Gender Gap Laid Bare

2021 ◽  
Vol 11 (7) ◽  
pp. 317
Author(s):  
Ismael Cabero ◽  
Irene Epifanio
Keyword(s):  
Gender Gap ◽  
Data Science ◽  
Academic Staff ◽  
Machine Learning Techniques ◽  
Academic Disciplines ◽  
University Environment ◽  
Learning Techniques ◽  
Spanish Universities ◽  
New Perspective

This paper presents a snapshot of the distribution of time that Spanish academic staff spend on different tasks. We carry out a statistical exploratory study by analyzing the responses provided in a survey of 703 Spanish academic staff in order to draw a clear picture of the current situation. This analysis considers many factors, including primarily gender, academic ranks, age, and academic disciplines. The tasks considered are divided into smaller activities, which allows us to discover hidden patterns. Tasks are not only restricted to the academic world, but also relate to domestic chores. We address this problem from a totally new perspective by using machine learning techniques, such as cluster analysis. In order to make important decisions, policymakers must know how academic staff spend their time, especially now that legal modifications are planned for the Spanish university environment. In terms of the time spent on quality of teaching and caring tasks, we expose huge gender gaps. Non-recognized overtime is very frequent.

scholarly journals A Review of AI and Data Science Support for Cancer Management

2020 ◽  
Author(s):  
E. Parimbelli ◽  
S. Wilk ◽  
R. Cornet ◽  
P. Sniatala ◽  
K. Sniatala ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Best Practices ◽  
Cancer Patients ◽  
Predictive Modeling ◽  
Data Science ◽  
Cancer Management ◽  
Research Challenges ◽  
Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

scholarly journals AI Enabled Data Quality Monitoring with Hydra

2021 ◽  
Vol 251 ◽  
pp. 04010
Author(s):  
Thomas Britton ◽  
David Lawrence ◽  
Kishansingh Rajput
Keyword(s):  
Artificial Intelligence ◽  
Data Quality ◽  
Data Science ◽  
Quality Monitoring ◽  
Alarm System ◽  
Future Outlook ◽  
Skill Sets ◽  
Early Results ◽  
Level Monitoring

Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how “off-the-shelf” technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.

Automated Machine Learning for Business

2021 ◽  
Author(s):  
Kai R. Larsen ◽  
Daniel S. Becker
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Graduate Program ◽  
School Curriculum ◽  
High School Curriculum ◽  
Stakeholder Groups ◽  
New Development ◽  
Automated Machine Learning ◽  
Undergraduate Programs

In Automated Machine Learning for Business, we teach the machine learning process using a new development in data science: automated machine learning. AutoML, when implemented properly, makes machine learning accessible to most people because it removes the need for years of experience in the most arcane aspects of data science, such as the math, statistics, and computer science skills required to become a top contender in traditional machine learning. Anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is one semester-long undergraduate course rather than a year in a graduate program, these tools will likely become a core component of undergraduate programs, and over time, even the high school curriculum.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

Enterprise Information Portals

2006 ◽  
pp. 277-290
Author(s):  
Wita Wojtkowski ◽  
Marshall Major
Keyword(s):  
Information Technology ◽  
New Technologies ◽  
Information Services ◽  
Legal Services ◽  
Law Firm ◽  
Enterprise Information

The focus of this case study is a successful regional law firm (an information intensive enterprise) that integrates information technology to improve the timeliness and quality of their work product. The firm uses information technology as an efficient and productive tool allowing them more time to understand their clients’ needs and envision where their businesses will be tomorrow. Their information services professionals evaluate new technologies with an eye toward improving delivery of legal services: the goal is to build an atmosphere where complex business is handled with ease. In this case we explore the issues related to the implementation of an enterprise portal. The issues are both technological and behavioral.

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